Purpose

Here we plot PCA and UMAP dimensionality reductions of the UMI count data from CellRanger and scUTRquant for comparison. We expect highly similar projections since the underlying data is identical.

Initialization

Libraries

library(magrittr)
library(tidyverse)
library(cowplot)
library(Matrix)
library(HDF5Array)
library(SingleCellExperiment)
library(scater)

Parameters

set.seed(20210818)
BiocParallel::register(BiocParallel::MulticoreParam(12))

Functions

plot_dimred_compare <- function (sce_x, sce_y, dimred="PCA",
                                 label_x="CellRanger", label_y="scUTRquant") {
  gx <- plotReducedDim(sce_x, dimred)
  gy <- plotReducedDim(sce_y, dimred)
  glabx <- ggdraw() + 
    draw_label(label_x, fontface='bold', size=16, hjust=0.36, vjust=0.5) +
    theme(plot.margin=margin(0, 0, 6, 0))
  glaby <- ggdraw() + 
    draw_label(label_y, fontface='bold', size=16, hjust=0.36, vjust=0.5) +
    theme(plot.margin=margin(0, 0, 6, 0))
  plot_grid(gx, gy, glabx, glaby, nrow=2, rel_heights=c(1, 0.05))
}

Data

Loading

df_counts_all <- readRDS("data/sce/processed/df_counts.all_genes.pca.Rds")

Preprocessing

Compute UMAP Coordinates

df_counts_all %<>%
  mutate(sce_sq=map(sce_sq, runUMAP),
         sce_10x=map(sce_10x, runUMAP))

Analysis

PCA

df_counts_all %>%
  transmute(sample_id, g=map2(sce_10x, sce_sq, plot_dimred_compare, dimred="PCA")) %>%
  deframe() %>% {
    for (id in names(.)) {
      title <- ggdraw() + 
        draw_label(id, fontface='bold', size=20, x=0.5, hjust=0.5) +
        theme(plot.margin=margin(0, 0, 0, 0))
      g <- plot_grid(title, .[[id]], ncol=1, rel_heights=c(0.1,1))
      
      ## print locally in this document
      print(g)
      
      ## save to PDFs
      # ggsave(sprintf("output/supplement/sup2-pca-compare-%s.pdf", id), 
      #        .[[id]], width=5, height=4, dpi=300)
    }
  }

UMAP

df_counts_all %>%
  transmute(sample_id, g=map2(sce_10x, sce_sq, plot_dimred_compare, dimred="UMAP")) %>%
  deframe() %>% {
    for (id in names(.)) {
      title <- ggdraw() + 
        draw_label(id, fontface='bold', size=20, x=0.5, hjust=0.5) +
        theme(plot.margin=margin(0, 0, 0, 0))
      g <- plot_grid(title, .[[id]], ncol=1, rel_heights=c(0.1,1))
      
      ## print locally in this document
      print(g)
      
      ## save to PDFs
      # ggsave(sprintf("output/supplement/sup2-pca-compare-%s.pdf", id), 
      #        .[[id]], width=5, height=4, dpi=300)
    }
  }

Conclusion

There is some structural similarity in the projections, but it is not immediately evident that exactly the same clusters are present. It is also not clear whether one mode is any more articulated than the other. In all samples, the first two PCs for CellRanger capture more variance than those for scUTRquant.


Runtime Details

Session Info

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_12/lib/libopenblasp-r0.3.12.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] scater_1.18.0               SingleCellExperiment_1.12.0
##  [3] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [5] GenomicRanges_1.42.0        GenomeInfoDb_1.26.0        
##  [7] HDF5Array_1.18.0            rhdf5_2.34.0               
##  [9] DelayedArray_0.16.0         IRanges_2.24.0             
## [11] S4Vectors_0.28.0            MatrixGenerics_1.2.0       
## [13] matrixStats_0.58.0          BiocGenerics_0.36.0        
## [15] Matrix_1.3-2                cowplot_1.1.0              
## [17] forcats_0.5.1               stringr_1.4.0              
## [19] dplyr_1.0.5                 purrr_0.3.4                
## [21] readr_1.4.0                 tidyr_1.1.3                
## [23] tibble_3.1.0                ggplot2_3.3.3              
## [25] tidyverse_1.3.0             magrittr_2.0.1             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6              fs_1.5.0                 
##  [3] lubridate_1.7.10          RcppAnnoy_0.0.18         
##  [5] httr_1.4.2                tools_4.0.2              
##  [7] backports_1.2.1           irlba_2.3.3              
##  [9] utf8_1.1.4                R6_2.5.0                 
## [11] vipor_0.4.5               uwot_0.1.10              
## [13] DBI_1.1.1                 colorspace_2.0-0         
## [15] rhdf5filters_1.2.0        withr_2.4.1              
## [17] gridExtra_2.3             tidyselect_1.1.0         
## [19] compiler_4.0.2            cli_2.3.1                
## [21] rvest_1.0.0               BiocNeighbors_1.8.0      
## [23] xml2_1.3.2                labeling_0.4.2           
## [25] scales_1.1.1              digest_0.6.27            
## [27] rmarkdown_2.7             XVector_0.30.0           
## [29] pkgconfig_2.0.3           htmltools_0.5.1.1        
## [31] sparseMatrixStats_1.2.0   highr_0.8                
## [33] dbplyr_2.1.0              rlang_0.4.10             
## [35] readxl_1.3.1              rstudioapi_0.13          
## [37] FNN_1.1.3                 DelayedMatrixStats_1.12.0
## [39] farver_2.1.0              generics_0.1.0           
## [41] jsonlite_1.7.2            BiocParallel_1.24.0      
## [43] BiocSingular_1.6.0        RCurl_1.98-1.2           
## [45] GenomeInfoDbData_1.2.4    scuttle_1.0.0            
## [47] ggbeeswarm_0.6.0          Rcpp_1.0.6               
## [49] munsell_0.5.0             Rhdf5lib_1.12.0          
## [51] fansi_0.4.2               viridis_0.5.1            
## [53] lifecycle_1.0.0           stringi_1.5.3            
## [55] yaml_2.2.1                zlibbioc_1.36.0          
## [57] grid_4.0.2                crayon_1.4.1             
## [59] lattice_0.20-41           haven_2.3.1              
## [61] beachmat_2.6.4            hms_1.0.0                
## [63] knitr_1.31                pillar_1.5.1             
## [65] codetools_0.2-18          reprex_1.0.0             
## [67] glue_1.4.2                evaluate_0.14            
## [69] modelr_0.1.8              vctrs_0.3.6              
## [71] cellranger_1.1.0          gtable_0.3.0             
## [73] assertthat_0.2.1          xfun_0.20                
## [75] rsvd_1.0.3                broom_0.7.5              
## [77] RSpectra_0.16-0           viridisLite_0.3.0        
## [79] beeswarm_0.3.1            ellipsis_0.3.1

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